Automated structure discovery in atomic force microscopy

Science Advances
Benjamin AlldrittAdam S Foster

Abstract

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules due to difficulties with interpretation of highly distorted AFM images originating from nonplanar molecules. Here, we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to applying high-resolution AFM to a large variety of systems, for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.

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Citations

Oct 9, 2020·Beilstein Journal of Nanotechnology·Linda LaflörPhilipp Rahe
Nov 3, 2020·Beilstein Journal of Nanotechnology·Jari JärviMilica Todorović
Apr 23, 2021·Nanoscale·Javier SotresJuan F Gonzalez-Martinez
Jul 3, 2021·Nanomaterials·Jaime Carracedo-CosmeRubén Pérez
Jul 7, 2021·Journal of the American Chemical Society·Jiali LiXiaonan Wang
Aug 14, 2021·Beilstein Journal of Nanotechnology·Zhipeng DouZeyu Xu
Sep 1, 2020·Nano Letters·Oliver M GordonPhilip J Moriarty
Sep 4, 2021·Beilstein Journal of Nanotechnology·Ido AzuriSidney R Cohen

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Methods Mentioned

BETA
AFM
nuclear magnetic resonance
scanning
atomic force microscopy

Software Mentioned

AFM
Spheres
Keras
Psi4
CO
vdW
TensorFlow

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